JP7174782B2 - Information processing device, information processing method and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

2. Description of the Related Art In recent years, with the rapid spread of the Internet, techniques related to analysis using various information on the Internet, for example, have been provided. For example, there has been proposed a technique for extracting information on needs for a target provided by a predetermined business operator based on a search query input by a user.

JP 2019-32776 A

However, it is not always possible to obtain useful information with the above-described conventional techniques. For example, in the above-described prior art, since the information on the needs for the target provided by the predetermined business operator is only extracted, the information on the latent needs for the target provided by the predetermined business operator can be visually and appropriately displayed. cannot be presented to

The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program capable of providing useful information.

An information processing apparatus according to the present application includes an acquisition unit that acquires actual usage data based on access to online content, a creation unit that creates a persona based on the actual usage data, and a usage state of the online content of the persona. an estimation unit for estimating; and an evaluation unit for quantitatively evaluating the usability test of the online content when estimating the usage situation , wherein the evaluation unit includes evaluation indexes and features when estimating the usage situation. It is characterized by scoring the improvement points of the online content from the degree .

According to one aspect of the embodiment, there is an effect that useful information can be obtained.

FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. FIG. 2 is an explanatory diagram showing the details of usage status estimation. FIG. 3 is a diagram illustrating a configuration example of an information processing system according to the embodiment; FIG. 4 is a diagram illustrating a configuration example of a terminal device according to the embodiment; FIG. 5 is a diagram illustrating a configuration example of an information processing apparatus according to the embodiment; FIG. 6 is a diagram showing an example of a user information database. FIG. 7 is a diagram showing an example of a history information database. FIG. 8 is a diagram showing an example of a persona information database. FIG. 9 is a diagram showing an example of the evaluation information database. FIG. 10 is a flowchart illustrating a processing procedure according to the embodiment; FIG. 11 is a diagram illustrating an example of a hardware configuration;

Embodiments for implementing an information processing apparatus, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail below with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Also, in the following embodiments, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

[1. Outline of information processing method]
First, an outline of an information processing method performed by an information processing apparatus according to an embodiment will be described with reference to FIG. FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. Note that FIG. 1 will be described by taking as an example a case of conducting a usability test for online content.

As shown in FIG. 1 , the information processing system 1 includes a terminal device 10 and an information providing device 100 . The terminal device 10 and the information providing device 100 are communicably connected to each other by wire or wirelessly via a network N (see FIG. 3).

The terminal device 10 is a smart device such as a smartphone or tablet used by a user U (user), and communicates with an arbitrary server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). It is a portable terminal device capable of performing The terminal device 10 has a screen such as a liquid crystal display, which has a touch panel function. Accepts various operations. An operation performed on an area where content is displayed on the screen may be an operation on the content. In addition, the terminal device 10 may be an information processing device such as a desktop PC (Personal Computer) or a notebook PC as well as a smart device.

The information providing device 100 cooperates with the terminal device 10 of each user U, and provides the terminal device 10 of each user U with API services and the like for various applications and various data. and is realized by a server device, a cloud system, or the like.

Also, the information providing device 100 may be an information processing device that provides some web service online to the terminal device 10 of each user U. FIG. For example, the information providing apparatus 100 provides web services such as Internet connection, search service, SNS (Social Networking Service), electronic commerce, electronic payment, online games, online banking, online trading, accommodation/ticket reservation, video/music distribution, and the like. service may be provided. In practice, the information providing apparatus 100 may cooperate with various servers that provide web services as described above and mediate web services.

Note that the information providing device 100 can acquire various types of information about the user U. FIG. For example, the information providing device 100 acquires information about attributes of the user U, such as the user's U gender, age, and area of residence. The information providing apparatus 100 stores and manages identification information indicating the user U (user ID, etc.) and information related to the attributes of the user U. FIG.

Further, the information providing apparatus 100 acquires various types of history information (log data) indicating actions of the user U from the terminal apparatus 10 of the user U or from various servers based on the user ID or the like. For example, the information providing device 100 acquires a location history, which is a history of the user U's location and date and time, from the terminal device 10 . The information providing apparatus 100 also acquires a search history, which is a history of search queries input by the user U, from a search server (search engine). Further, the information providing apparatus 100 acquires a viewing history, which is a history of content viewed by the user U, from the content server. In addition, the information providing apparatus 100 acquires the purchase history, which is the history of product purchases of the user U, from the electronic commerce server or the settlement server. Further, the information providing apparatus 100 may acquire the exhibition history and sales history, which are the history of the user U's exhibition in the marketplace, from the electronic commerce server or the settlement server. In addition, the information providing apparatus 100 acquires the posting history, which is the posting history of the user U, from a posting server that provides a word-of-mouth posting service or an SNS server.

[1-1. basic action〕
In this embodiment, the information providing apparatus 100 automatically performs quantitative evaluation of usability tests of online contents (websites, applications, etc.). Here, we use personas. A persona is a fictitious image of a user (user image) who is (or is considered to be) interested in the company's services or products. Then, when a persona is set, a fictitious user image is materialized with granularity such as age, gender, occupation, annual income, area of residence, and family composition.

For example, as shown in FIG. 1, the information providing apparatus 100 estimates a user attribute from an inflow query of a website or the like and a user ID, and creates a persona (step S1). For example, the information providing apparatus 100 obtains information about the category and the degree of similarity with previous and subsequent search queries from the inflow query. Also, from the user ID, user information, various history information, etc. are obtained. Inflow queries and user IDs are merely examples. Actually, any information that can be used for attribute estimation and persona creation of one user (one user) may be used.

In the present embodiment, the information providing apparatus 100 creates a learning device before estimating the usage status in step S2, which will be described later. Acquire data (user information, website operation logs, etc.) that can be obtained at the time, and create correct data and a correct persona. For example, a correct persona is a user who behaves according to the scenario of the scenario walk-through method, such as what kind of search query is used for searching and what kind of page transition is performed. In the example shown in FIG. 1, the information providing apparatus 100 creates three different personas (correct persona candidates), persona A, persona B, and persona C. FIG. For example, any one of persona A, persona B, and persona C may be set as a provisional correct persona, and the others may be set as candidate personas to be compared.

Subsequently, the information providing apparatus 100 creates a learning device based on the persona, and estimates the usage status of the website or the like by machine learning (step S2). Here, for convenience of explanation, the classifier (learned model) is also explained as a part of the learner without distinguishing between the learner and the classifier (learned model) in the mechanism of machine learning. In practice, the learning process and the judgment process (estimation process) are separated, and the learner compares the estimated results with the actual results and adjusts the parameters to perform judgment and estimation according to the input data. It may be one that adjusts the device (learned model).

In the present embodiment, a plurality of correct personas are created as described above, and a usage classification learning device is created from actual usage data such as inflow queries from websites and user attributes. In the example shown in FIG. 1, the information providing apparatus 100 creates in advance three correct personas with different data, persona A, persona B, and persona C, and inputs them to the learning device. categorized into For convenience of explanation, persona A is classified into usage situation 1, persona B into usage situation 2, and persona C into usage situation 3. FIG. That is, the correct persona and the usage status are linked. In this way, the information providing apparatus 100 trains the learning device so that when correct data indicating the correct persona is input to the learning device, information indicating the usage status corresponding to the correct persona is output.

Then, the information providing apparatus 100 estimates the usage status by classifying the usage status by performing machine learning based on the user information, the operation log of the website, and the like. In the example shown in FIG. 1, the information providing apparatus 100 inputs actually obtained data to the learning device, and if the input data corresponds to any of Persona A, Persona B, and Persona C, Classify into one of usage situations 1 to 3 corresponding to the persona. If none of the above applies, the persona is classified as other persona (unexpected persona) as usage 4.

Here, with reference to FIG. 2, details of usage status estimation will be described. FIG. 2 is an explanatory diagram showing the details of usage status estimation. As shown in FIG. 2, the information providing apparatus 100 inputs a correct persona created in advance using correct answer data into a learning device and learns it. Then, the actually obtained data is input to a learning device, and the usage status is estimated by classifying the usage status according to the corresponding correct persona. Here, as data, attribute data and action data are exemplified. Attribute data includes demographics (demographic attributes) and user profiles. Action data includes PV (Page View), clicks, and the like. In the example of FIG. 2, the usage situations 1 to 3 are assumed personas, but the usage situation 4 is a persona that is not assumed. In terms of the number of classifications (the number of times the input data is classified), usage status 4 is the largest, followed by usage status 2, followed by usage status 1 and usage status 3.

Subsequently, the information providing apparatus 100 automatically performs a quantitative evaluation of the usability test of the website, etc. (step S3). For example, the information providing apparatus 100 scores improvement points of a website or the like based on the evaluation index and the degree of feature (page transition, stay time, search query, etc.) when estimating the usage status.

Subsequently, the information providing device 100 provides the terminal device 10 of the user U with information about the quantitative evaluation result of the usability test of the website, etc. (step S4).

In this embodiment, the information providing apparatus 100 evaluates whether the user is operating the website or the like as expected (according to the persona) based on the accuracy of the learning device when estimating the usage status. As a result of the evaluation, it is possible to grasp the improvement points of the website etc. from the feature degree with low contribution. In addition, since the gap between the expected usage and the actual usage can be found, the accuracy of usability test design will be improved. By sequentially repeating the above processes, the usability of the website and the accuracy of the test are improved.

It is also possible to incorporate it into a mechanism such as an AB test (A/B testing). That is, in this embodiment, it is also possible to perform an A/B test of personas instead of websites or the like. This makes it possible to improve the accuracy of the persona (persona brush-up).

For example, the information providing apparatus 100 compares a persona obtained from the user's actual behavior (actual user persona) with a persona set in advance as a user image (set persona), and if there is a discrepancy, scores the improvement points of the website etc. in the direction of bringing the actual persona closer to the set persona. Specifically, by adding a certain function, a score is given to how close the actual user persona was to the set persona. Alternatively, the information providing apparatus 100 modifies the set persona so as to bring it closer to the actual persona. For example, a set of actual usage data and usage status is input to a learning device and machine learning is performed to create a new correct persona. In other words, you can think about actions by looking at the divergence and changes between personas. In this embodiment, it is assumed that the persona is transformed, and the change is captured.

[2. Configuration example of information processing system]
Next, the configuration of the information processing system 1 including the information providing device 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 3, the information processing system 1 according to the embodiment includes a terminal device 10 and an information providing device 100. As shown in FIG. These various devices are communicatively connected via a network N by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

Also, the number of devices included in the information processing system 1 shown in FIG. 3 is not limited to the illustrated one. For example, only one terminal device 10 is shown in FIG. 3 for simplification of illustration, but this is only an example and is not limited, and two or more devices may be provided.

The terminal device 10 is an information processing device used by the user U. FIG. For example, the terminal device 10 may be a smart device such as a smartphone or a tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game machine with a communication function, a car navigation system, a smart watch, or a head-mounted display. and other wearable devices, smart glasses, and the like.

In addition, the terminal device 10 is compatible with wireless communication networks such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: fifth generation mobile communication system), Bluetooth (registered trademark), wireless LAN (Local It is possible to communicate with the information providing apparatus 100 by connecting to the network N via short-range wireless communication such as Area Network).

The information providing device 100 is, for example, a PC, a server device, a mainframe, a workstation, or the like. Note that the information providing apparatus 100 may be realized by cloud computing.

[3. Configuration example of terminal device]
Next, the configuration of the terminal device 10 will be described with reference to FIG. FIG. 4 is a diagram showing a configuration example of the terminal device 10. As shown in FIG. As shown in FIG. 4, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40. Prepare.

(Communication unit 11)
The communication unit 11 is connected to the network N (see FIG. 3) by wire or wirelessly, and transmits and receives information to and from the information providing apparatus 100 via the network N. FIG. For example, the communication unit 11 is implemented by a NIC (Network Interface Card), an antenna, or the like.

(Display unit 12)
The display unit 12 is a display device that displays various information such as position information. For example, the display unit 12 is a liquid crystal display (LCD) or an organic EL display (Organic Electro-Luminescent Display). Also, the display unit 12 is a touch panel display, but is not limited to this.

(Input unit 13)
The input unit 13 is an input device that receives various operations from the user U. Further, the input unit 13 has, for example, buttons and the like for inputting characters, numbers, and the like. Moreover, when the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13 . Note that the input unit 13 may be a microphone or the like that receives voice input from the user U. FIG. The microphone may be wireless.

(Positioning unit 14)
The positioning unit 14 receives signals (radio waves) transmitted from GPS (Global Positioning System) satellites, and based on the received signals, position information (for example, latitude and longitude). That is, the positioning unit 14 positions the position of the terminal device 10 . GPS is merely an example of GNSS (Global Navigation Satellite System).

Also, the positioning unit 14 can measure the position by various methods other than GPS. For example, the positioning unit 14 may measure the position using various communication functions of the terminal device 10 as described below as auxiliary positioning means for position correction and the like.

(Wi-Fi positioning)
For example, the positioning unit 14 measures the position of the terminal device 10 using the Wi-Fi (registered trademark) communication function of the terminal device 10 or the communication network provided by each communication company. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the position of the terminal device 10 by measuring the distance to a nearby base station or access point.

(beacon positioning)
The positioning unit 14 may also use the Bluetooth (registered trademark) function of the terminal device 10 to measure the position. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting with a beacon transmitter connected by the Bluetooth (registered trademark) function.

(geomagnetic positioning)
Further, the positioning unit 14 positions the position of the terminal device 10 based on the geomagnetism pattern of the structure measured in advance and the geomagnetic sensor provided in the terminal device 10 .

(RFID positioning)
Further, for example, if the terminal device 10 has an RFID (Radio Frequency Identification) tag function equivalent to a contactless IC card used at station ticket gates, stores, etc., or has a function of reading an RFID tag In this case, the location used is recorded together with the information that the payment was made by the terminal device 10 . The positioning unit 14 may measure the position of the terminal device 10 by acquiring such information. Also, the position may be measured by an optical sensor provided in the terminal device 10, an infrared sensor, or the like.

The positioning unit 14 may measure the position of the terminal device 10 using one or a combination of the positioning means described above, if necessary.

(Sensor unit 20)
The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10 . The connection may be wired connection or wireless connection. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices and wireless devices. In the example shown in FIG. 4, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, an atmospheric pressure sensor 23, an air temperature sensor 24, a sound sensor 25, an optical sensor 26, a magnetic sensor 27, and an image sensor ( camera) 28.

The sensors 21 to 28 described above are only examples and are not limited. That is, the sensor unit 20 may be configured to include a part of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28. .

The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10 such as movement direction, speed, and acceleration of the terminal device 10 . The gyro sensor 22 detects physical movements of the terminal device 10 such as inclination in three axial directions based on the angular velocity of the terminal device 10 and the like. The atmospheric pressure sensor 23 detects the atmospheric pressure around the terminal device 10, for example.

Since the terminal device 10 includes the above-described acceleration sensor 21, gyro sensor 22, barometric pressure sensor 23, etc., techniques such as pedestrian dead-reckoning (PDR: Pedestrian Dead-Reckoning) using these sensors 21 to 23, etc. , the position of the terminal device 10 can be determined. This makes it possible to acquire indoor position information that is difficult to acquire with a positioning system such as GPS.

For example, a pedometer using the acceleration sensor 21 can calculate the number of steps, walking speed, and distance walked. Further, by using the gyro sensor 22, it is possible to know the traveling direction, the direction of the line of sight, and the inclination of the body of the user U. Also, from the atmospheric pressure detected by the atmospheric pressure sensor 23, the altitude at which the terminal device 10 of the user U is present and the number of floors can be known.

The temperature sensor 24 detects the temperature around the terminal device 10, for example. The sound sensor 25 detects sounds around the terminal device 10, for example. The optical sensor 26 detects the illuminance around the terminal device 10 . The magnetic sensor 27 detects, for example, geomagnetism around the terminal device 10 . The image sensor 28 captures an image around the terminal device 10 .

The atmospheric pressure sensor 23, the temperature sensor 24, the sound sensor 25, the optical sensor 26, and the image sensor 28 described above detect the atmospheric pressure, temperature, sound, and illuminance, respectively, or capture an image of the surroundings to detect the terminal device 10. It is possible to detect the surrounding environment and situations. In addition, it is possible to improve the accuracy of the location information of the terminal device 10 based on the surrounding environment and situation of the terminal device 10 .

(control unit 30)
The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, an input/output port, and various circuits. Also, the control unit 30 may be configured by hardware such as an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmission unit 31 , a reception unit 32 , a processing unit 33 and a display control unit 34 .

(Sending unit 31)
The transmission unit 31 receives, for example, various information input by the user U using the input unit 13, various information detected by the sensors 21 to 28 mounted on or connected to the terminal device 10, and information measured by the positioning unit 14. The position information of the terminal device 10 and the like can be transmitted to the information providing device 100 via the communication unit 11 .

(Receiver 32)
The receiving unit 32 can receive various information provided by the information providing apparatus 100 and requests for various information from the information providing apparatus 100 via the communication unit 11 .

(Processing unit 33)
The processing unit 33 controls the entire terminal device 10 including the display unit 12 and the like. For example, the processing unit 33 can output various types of information transmitted by the transmitting unit 31 and various types of information received by the receiving unit 32 from the information providing apparatus 100 to the display unit 12 for display.

(storage unit 40)
The storage unit 40 is realized by, for example, a semiconductor memory device such as RAM (Random Access Memory) or flash memory, or a storage device such as HDD (Hard Disk Drive), SSD (Solid State Drive), or optical disk. be. Various programs, various data, and the like are stored in the storage unit 40 .

[4. Configuration example of information processing device]
Next, the configuration of the information providing device 100 according to the embodiment will be described using FIG. FIG. 5 is a diagram showing a configuration example of the information providing device 100 according to the embodiment. As shown in FIG. 5, the information providing device 100 has a communication section 110, a storage section 120, and a control section .

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Also, the communication unit 110 is connected to the network N (see FIG. 3) by wire or wirelessly.

(storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 5, the storage unit 120 has a user information database 121, a history information database 122, a persona information database 123, and an evaluation information database .

(User information database 121)
The user information database 121 stores various information about the user U. FIG. For example, the user information database 121 stores various information such as user U attributes. FIG. 6 is a diagram showing an example of the user information database 121. As shown in FIG. In the example shown in FIG. 6, the user information database 121 has items such as "user ID (Identifier)", "age", "sex", "home", "place of work", and "interest".

“User ID” indicates identification information for identifying the user U. "Age" indicates the age of the user U identified by the user ID. Note that the “age” may be a specific age of the user U identified by the user ID, such as 35 years old. "Gender" indicates the gender of the user U identified by the user ID.

"Home" indicates location information of the home of the user U identified by the user ID. In the example shown in FIG. 6, "home" is represented by an abstract code such as "LC11", but may be latitude/longitude information or the like. Also, for example, "home" may be an area name or an address.

"Place of work" indicates location information of the place of work (school in the case of a student) of the user U identified by the user ID. In the example shown in FIG. 6, the "place of work" is illustrated as an abstract code such as "LC12", but may be latitude/longitude information or the like. Also, for example, the "place of work" may be an area name or an address.

"Interest" indicates the interest of the user U identified by the user ID. That is, "interest" indicates an object in which the user U identified by the user ID is highly interested. For example, the "interest" may be a search query (keyword) that the user U has entered into a search engine and searched for. In the example shown in FIG. 6, one "interest" is shown for each user U, but there may be more than one.

For example, in the example shown in FIG. 6, the age of the user U identified by the user ID "U1" is "twenties" and the gender is "male". Also, for example, the user U identified by the user ID "U1" indicates that the home is "LC11". Also, for example, the user U identified by the user ID "U1" indicates that the place of work is "LC12". Also, for example, the user U identified by the user ID "U1" indicates that he is interested in "sports".

Here, in the example shown in FIG. 6 , abstract values such as “U1”, “LC11” and “LC12” are used, but “U1”, “LC11” and “LC12” have specific values. It is assumed that information such as character strings and numerical values is stored. Hereinafter, abstract values may also be illustrated in diagrams relating to other information.

The user information database 121 is not limited to the above, and may store various types of information depending on the purpose. For example, the user information database 121 may store various types of information regarding the user U's terminal device 10 . In addition, the user information database 121 stores user U's demographics (demographic attributes), psychographics (psychological attributes), geographics (geographical attributes), behavioral attributes (behavioral attributes), etc. Information about attributes may be stored. For example, the user information database 121 includes name, family structure, hometown (local), occupation, position, income, qualification, residence type (detached house, condominium, etc.), presence or absence of car, commuting time, commuting time, commuting time. Information such as routes, commuter pass sections (stations, lines, etc.), frequently used stations (other than the nearest station to your home or place of work), lessons (places, time zones, etc.), hobbies, interests, lifestyle, etc. may

(History information database 122)
The history information database 122 stores various types of information related to history information (log data) indicating user U's actions. FIG. 7 is a diagram showing an example of the history information database 122. As shown in FIG. In the example shown in FIG. 7, the history information database 122 has items such as "user ID", "location history", "search history", "browsing history", "purchase history", and "posting history".

“User ID” indicates identification information for identifying the user U. "Position history" indicates a position history, which is a history of the user's U position and movement. Also, "search history" indicates a search history that is a history of search queries input by the user U. FIG. "Browsing history" indicates a browsing history that is a history of contents browsed by the user U. FIG. "Purchase history" indicates the purchase history of the user U's purchases. In addition, “posting history” indicates a posting history that is a history of posts by the user U. FIG. In addition, the “posting history” may include questions about user U's property.

For example, in the example shown in FIG. 7, the user U identified by the user ID "U1" moves along the "location history #1", searches along the "search history #1", and performs the "viewing". It indicates that the content was browsed according to the "Purchase history #1", the predetermined product etc. was purchased at the predetermined store etc. according to the "Purchase history #1", and the content was posted according to the "Posting history".

Here, in the example shown in FIG. 7, abstract history such as "U1", "location history #1", "search history #1", "browsing history #1", "purchase history #1" and "posting history #1" "U1", "location history #1", "search history #1", "browsing history #1", "purchase history #1" and "posting history #1" , information such as specific character strings and numerical values are stored.

Note that the history information database 122 may store various types of information, not limited to the above, depending on the purpose. For example, the history information database 122 may store the user U's usage history of a predetermined service. In addition, the history information database 122 may store the user U's store visit history, facility visit history, and the like. Also, the history information database 122 may store a history of user U's electronic payment and the like.

(Persona information database 123)
The persona information database 123 stores various types of information about personas that indicate assumed users U. FIG. FIG. 8 is a diagram showing an example of the persona information database 123. As shown in FIG. In the example shown in FIG. 8, the persona information database 123 has items such as "set persona ID", "content", "attribute data", "behavior data", and "usage".

The “set persona ID” indicates identification information for identifying a set persona (correct persona) that is set based on an assumed user image. "Contents" indicates online contents to be accessed by the set persona, such as websites and applications. Also, "attribute data" indicates attribute data used to create the setting persona. Also, "behavior data" indicates the behavior data used to create the set persona. That is, this attribute data and action data are correct data. "Usage status" indicates the usage status in which the set persona was classified when the learner was created.

For example, in the example shown in FIG. 8, the persona identified by the set persona ID "set persona #1" uses "content #1", has "attribute data #1", and has "behavior data #1". It is an image of a user who behaves according to and is classified into "Usage Situation #1".

Here, in the example shown in FIG. 8, abstract values such as "setting persona #1", "content #1", "attribute data #1", "behavior data #1", and "usage situation #1" are used. , but specific character strings, numerical values, etc. Information shall be stored.

It should be noted that the persona information database 123 may store various types of information, not limited to the above, depending on the purpose. For example, the persona information database 123 may store information about the date and time when the set persona was created. The persona information database 123 may store information about AB test results of a plurality of set personas.

(Evaluation information database 124)
The evaluation information database 124 stores various types of information about the persona indicating the assumed user U. FIG. FIG. 9 is a diagram showing an example of the evaluation information database 124. As shown in FIG. In the example shown in FIG. 9, the evaluation information database 124 includes “actual user persona ID”, “content”, “attribute data”, “behavior data”, “applicable persona”, “usage”, “divergence”, It has an item such as "evaluation".

“Actual use persona ID” indicates identification information for identifying an actual use persona created based on actual data. "Contents" indicates online contents to be accessed by the actual user persona, such as websites and applications. Also, "attribute data" indicates the attribute data used to create the actual use persona. Also, "behavior data" indicates the behavior data used to create the actual use persona. Also, the "applicable persona" indicates identification information for identifying a set persona that corresponds to (or is close to) the actual use persona. If there is no set persona corresponding to the actual persona, nothing is stored (blank). "Usage status" indicates the usage status of the actual persona estimated by inputting the attribute data and behavior data into the learning device. For example, "Usage status" indicates the usage status of the set persona that corresponds to (or is close to) the actual usage persona. Further, the "degree of divergence" indicates the degree of divergence between the set persona and the actually used persona. "Evaluation" indicates the results of quantitative evaluation of online content usability tests. For example, the "evaluation" may be a value (score) obtained by scoring improvement points of the online content based on the evaluation index and feature degree when estimating the usage status.

For example, in the example shown in FIG. 9, the persona identified by the actual-use persona ID “actual-use persona #1” uses “content #1”, has “attribute data #1”, and has “behavior data #1”. 1”, corresponds to “set persona #1”, is presumed to be “usage situation #1”, and has only “set persona #1” and “deviation #1” It deviates, indicating that it has a rating of “Rating #1”.

Here, in the example shown in FIG. 9, "actual user persona #1", "content #1", "attribute data #1", "behavior data #1", "corresponding persona #1", "usage situation #1" ”, “degree of deviation #1”, and “evaluation #1” are used for illustration, but “actual user persona #1”, “content #1”, “attribute data #1”, “behavior data #1”, “corresponding persona #1”, “usage #1”, “degree of divergence #1”, and “evaluation #1” shall store information such as specific character strings and numerical values. .

It should be noted that the evaluation information database 124 may store various types of information, not limited to the above, depending on the purpose. For example, the evaluation information database 124 may store information related to the evaluation index and feature degree when estimating the usage status. The evaluation information database 124 may also store information regarding improvement suggestions based on improvements in online content.

(control unit 130)
Returning to FIG. 5, the description is continued. The control unit 130 is a controller, and for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like controls the information providing apparatus 100. Various programs (corresponding to an example of an information processing program) stored in the internal storage device are executed by using a storage area such as a RAM as a work area. In the example shown in FIG. 5 , the control unit 130 has an acquisition unit 131 , a creation unit 132 , an estimation unit 133 , an evaluation unit 134 and a provision unit 135 .

(Acquisition unit 131)
Acquisition unit 131 acquires actual use data based on access to online content (websites, applications, etc.) via communication unit 110 . For example, the acquisition unit 131 acquires data (user information, operation logs of the website, etc.) that can be obtained when the website or the like is operated.

At this time, the acquisition unit 131 acquires various information about the user U via the communication unit 110 . For example, the acquisition unit 131 acquires information indicating the user U (user ID, etc.), location information of the user U, attribute information of the user U, and the like. Acquisition unit 131 then registers in user information database 121 of storage unit 120 .

In addition, the acquisition unit 131 acquires various types of history information (log data) indicating actions of the user U via the communication unit 110 . Acquisition unit 131 then registers in history information database 122 of storage unit 120 .

(Creating unit 132)
The creating unit 132 creates a persona based on actual usage data. Note that the creating unit 132 creates the correct persona in advance. At this time, the creation unit 132 may create a correct persona according to an instruction or data transmission from the terminal device 10 of the user U. Note that the creating unit 132 may be a UI (User Interface) for creating a persona.

(Estimation unit 133)
The estimating unit 133 estimates how the persona uses the online content. For example, the estimating unit 133 classifies the usage status of the online content of the correct persona in advance, compares the correct persona and the persona, and estimates the usage status of the persona. At this time, the estimating unit 133 classifies the usage status of the online content of each of the plurality of correct personas in advance, compares each of the plurality of correct personas with the persona, and estimates the usage status of the persona. may

In addition, when the correct persona and the persona diverge, the estimating unit 133 may correct the correct persona so that the correct persona approaches the persona.

The estimation unit 133 also creates a learning device from the actual usage data used when creating the persona, and performs machine learning to estimate the usage status. For example, the estimating unit 133 learns the learning device so that, when correct data indicating the correct persona is input to the learning device, information indicating the usage status corresponding to the correct persona is output.

In this embodiment, the estimation unit 133 creates a usage estimation model as a learning device by machine learning using RNN (Recurrent Neural Network), LSTM (Long short-term memory), or the like. As for the model, a model of any type and any format can be adopted. For example, the estimation unit 133 may adopt SVM (Support Vector Machine) or DNN (Deep Neural Network) as a model. Here, DNN may be CNN (Convolutional Neural Network) or RNN. The model may also be a model realized by combining a plurality of models, such as a model combining CNN and RNN.

Note that the RNN and LSTM may be neural networks based on an attention mechanism. Also, the estimation unit 133 may use a similar natural language processing model. Data mining and other machine learning algorithms may also be used. The estimating unit 133 can perform model learning using the various learning methods described above.

(Evaluation unit 134)
The evaluation unit 134 performs a quantitative evaluation of the usability test of the online content when estimating the usage status. For example, the evaluation unit 134 scores improvement points of the online content based on the evaluation index and feature degree when estimating the usage status.

In addition, when the correct persona and the persona diverge, the evaluation unit 134 may score improvements in the online content so that the persona approaches the correct persona.

In addition, the evaluation unit 134 may perform an AB test on a plurality of correct personas, evaluate the correct personas, and score them.

Also, the evaluation unit 134 evaluates whether the user is operating the online content as expected based on the accuracy of the learning device when the usage situation is classified.

(Providing unit 135)
The providing unit 135 provides the terminal device 10 of the user U with information about the quantitative evaluation result of the usability test of the online content via the communication unit 110 . At this time, the providing unit 135 may provide information regarding the quantitative evaluation result of the usability test of the online content in response to the user U's request for information provision.

[5. Processing procedure]
Next, a processing procedure performed by the information providing apparatus 100 according to the embodiment will be described with reference to FIG. 10 . FIG. 10 is a flowchart illustrating a processing procedure according to the embodiment; Note that the processing procedure described below is repeatedly executed by the control unit 130 of the information providing apparatus 100 .

As shown in FIG. 10, the acquisition unit 131 of the information providing apparatus 100 acquires actual use data based on access to online content (websites, applications, etc.) via the communication unit 110 (step S101).

Subsequently, the creation unit 132 of the information providing apparatus 100 creates correct data from the actual use data, and creates a correct persona in advance based on the user image assumed based on the correct data (step S102).

Subsequently, the estimating unit 133 of the information providing apparatus 100 creates a usage classification learning device from the correct data and the correct persona (step S103). At this time, the estimating unit 133 inputs the correct data and the correct persona to the learning device, and learns to classify it into a predetermined usage situation. For example, the estimating unit 133 learns the learning device so that, when correct data indicating the correct persona is input to the learning device, information indicating the usage status corresponding to the correct persona is output.

Subsequently, the acquisition unit 131 of the information providing device 100 acquires actual use data based on the access to the online content via the communication unit 110 (step S104).

Subsequently, the creating unit 132 of the information providing device 100 creates a persona based on the actual usage data (step S105).

Subsequently, the estimation unit 133 of the information providing apparatus 100 estimates the usage status of the online content of the persona (step S106). For example, the estimating unit 133 classifies the usage status of the online content of the correct persona in advance, compares the correct persona and the persona, and estimates the usage status of the persona. At this time, the estimation unit 133 inputs the actual usage data and the persona to the learning device, and estimates the usage status of the persona from the output usage status classification result.

Subsequently, the evaluation unit 134 of the information providing apparatus 100 performs a quantitative evaluation of the usability test of the online content when estimating the usage status (step S107). For example, the evaluation unit 134 scores improvement points of the online content based on the evaluation index and feature degree when estimating the usage status.

Subsequently, the evaluation unit 134 of the information providing apparatus 100 performs an AB test of the correct personas, and determines the correct persona to which the largest number of personas correspond among the plurality of correct personas (step S108). At this time, the evaluation unit 134 counts and evaluates the number of personas that correspond to each of the plurality of correct personas (the number of personas classified: the number of classifications).

Subsequently, the providing unit 135 of the information providing device 100 provides the terminal device 10 of the user U via the communication unit 110 with information regarding the quantitative evaluation result of the usability test of the online content (step S109). .

[6. Modification]
The terminal device 10 and the information providing device 100 described above may be implemented in various different forms other than the above embodiments. So, below, the modification of embodiment is demonstrated.

In the above embodiment, part or all of the processing executed by the information providing device 100 may actually be executed by the terminal device 10 . For example, the processing may be completed stand-alone (by the terminal device 10 alone). In this case, it is assumed that the terminal device 10 has the functions of the information providing device 100 in the above embodiment. In addition, in the above-described embodiment, the terminal device 10 cooperates with the information providing device 100, so from the user U's point of view, it appears that the terminal device 10 is executing the processing of the information providing device 100 as well. That is, from another point of view, it can be said that the terminal device 10 includes the information providing device 100 .

In the above embodiment, the information providing apparatus 100 may create a persona estimation learner from actual usage data based on access to online content. For example, the information providing apparatus 100 performs learning in advance using a set of correct data and a correct persona as input data, and creates a learning device that outputs a correct persona when correct data is input. Then, the information providing apparatus 100 inputs the actual use data to the learning device, and uses the output persona as the actual use persona. If there is no correct persona corresponding to the input actual use data and no persona is output, the information providing apparatus 100 creates a new correct persona based on the data.

[7. effect〕
As described above, the information providing apparatus 100 according to the present application includes the acquisition unit 131 that acquires actual usage data based on access to online content, the creation unit 132 that creates personas based on the actual usage data, and the persona An estimation unit 133 for estimating the usage status of online content, and an evaluation unit 134 for quantitatively evaluating the usability test of the online content when estimating the usage status.

In addition, the evaluation unit 134 scores improvements of the online content based on the evaluation index and feature degree when estimating the usage status.

Also, the creating unit 132 creates a correct persona in advance. Then, the estimation unit 133 classifies the usage status of the online content of the correct persona in advance, compares the correct persona and the persona, and estimates the usage status of the persona.

In addition, when the correct persona and the persona diverge, the evaluation unit 134 may score improvements in the online content so that the persona approaches the correct persona.

In addition, when the correct persona and the persona diverge, the estimating unit 133 may correct the correct persona so that the correct persona approaches the persona.

Also, the creating unit 132 creates a plurality of correct personas in advance. Then, the estimation unit 133 classifies in advance the usage status of online content for each of the plurality of correct personas, compares each of the plurality of correct personas with the persona, and estimates the usage status of the persona.

In addition, the evaluation unit 134 may perform an AB test on a plurality of correct personas, evaluate the correct personas, and score them.

The estimation unit 133 also creates a learning device from the actual usage data used when creating the persona, and performs machine learning to estimate the usage status.

Also, the evaluation unit 134 evaluates whether the user is operating the online content as expected based on the accuracy of the learning device when the usage situation is classified.

Through any one or a combination of the processes described above, the information providing apparatus 100 according to the present application and the terminal apparatus 10 capable of communicating therewith can obtain useful information. For example, quantitative evaluation of usability testing of online content can be done automatically. In addition, it is possible to classify usage situations by performing machine learning based on information of users who created personas and operation logs of websites. In addition, it is possible to evaluate whether the user is operating the website as expected from the accuracy of the learning device when classifying. In addition, it is possible to grasp the improvement points of the website from the low-contribution feature. In addition, since the gap between the expected usage and the actual usage can be found, the accuracy of usability test design will be improved. The sequential iteration improves the usability of the website and the accuracy of the test. It is also possible to incorporate it into a mechanism such as an AB test.

[8. Hardware configuration]
Also, the terminal device 10 and the information providing device 100 according to the above-described embodiments are implemented by a computer 1000 configured as shown in FIG. 11, for example. The information providing apparatus 100 will be described below as an example. FIG. 11 is a diagram illustrating an example of a hardware configuration; The computer 1000 is connected to an output device 1010 and an input device 1020, and an arithmetic device 1030, a primary storage device 1040, a secondary storage device 1050, an output I/F (Interface) 1060, an input I/F 1070, and a network I/F 1080 are buses. It has a form connected by 1090.

The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic unit 1030 is implemented by, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), or the like.

The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that temporarily stores data used for various calculations by the arithmetic device 1030 . The secondary storage device 1050 is a storage device in which data used for various calculations by the arithmetic device 1030 and various databases are registered. State Drive), flash memory, or the like. The secondary storage device 1050 may be an internal storage or an external storage. Also, the secondary storage device 1050 may be a removable storage medium such as a USB memory or an SD (Secure Digital) memory card. Also, the secondary storage device 1050 may be a cloud storage (online storage), a NAS (Network Attached Storage), a file server, or the like.

The output I/F 1060 is an interface for transmitting information to be output to the output device 1010 that outputs various information such as a display, a projector, and a printer. (Digital Visual Interface) and HDMI (registered trademark) (High Definition Multimedia Interface). Also, the input I/F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, keypad, buttons, scanner, etc., and is realized by, for example, USB.

Also, the output I/F 1060 and the input I/F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

Also, the output device 1010 and the input device 1020 may be integrated like a touch panel. In this case, the output I/F 1060 and the input I/F 1070 may also be integrated as an input/output I/F.

Note that the input device 1020 includes, for example, optical recording media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), PDs (Phase change rewritable discs), magneto-optical recording media such as MOs (Magneto-Optical discs), and tapes. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like.

Network I/F 1080 receives data from other devices via network N and sends the data to arithmetic device 1030, and also transmits data generated by arithmetic device 1030 via network N to other devices.

Arithmetic device 1030 controls output device 1010 and input device 1020 via output I/F 1060 and input I/F 1070 . For example, arithmetic device 1030 loads a program from input device 1020 or secondary storage device 1050 onto primary storage device 1040 and executes the loaded program.

For example, when the computer 1000 functions as the information providing device 100 , the arithmetic device 1030 of the computer 1000 implements the functions of the control unit 130 by executing a program loaded on the primary storage device 1040 . Further, arithmetic device 1030 of computer 1000 may load a program acquired from another device via network I/F 1080 onto primary storage device 1040 and execute the loaded program. Further, the arithmetic unit 1030 of the computer 1000 may cooperate with another device via the network I/F 1080, and call functions, data, etc. of the program from another program of the other device for use.

[9. others〕
Although the embodiments of the present application have been described above, the present invention is not limited by the contents of these embodiments. In addition, the components described above include those that can be easily assumed by those skilled in the art, those that are substantially the same, and those within the so-called equivalent range. Furthermore, the components described above can be combined as appropriate. Furthermore, various omissions, replacements, or modifications of components can be made without departing from the gist of the above-described embodiments.

Further, among the processes described in the above embodiments, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being performed manually can be performed manually. All or part of this can also be done automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the illustrated one, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, the information providing apparatus 100 described above may be implemented by a plurality of server computers, and depending on the function, may be implemented by calling an external platform or the like using an API (Application Programming Interface), network computing, or the like. can be changed flexibly.

Also, the above-described embodiments and modifications can be appropriately combined within a range that does not contradict the processing content.

Also, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

1 information processing system 10 terminal device 30 control unit 31 transmission unit 32 reception unit 33 processing unit 100 information providing device 110 communication unit 120 storage unit 121 user information database 122 history information database 123 persona information database 124 evaluation information database 130 control unit 131 Acquisition unit 132 Creation unit 133 Estimation unit 134 Evaluation unit 135 Provider

Claims (17)

an acquisition unit for acquiring usage data based on access to online content;
a creation unit that creates a persona based on the actual usage data;
an estimation unit that estimates usage of the online content by the persona;
an evaluation unit that quantitatively evaluates the usability test of the online content when estimating the usage situation;
with
The evaluation unit scores points to be improved in the online content based on the evaluation index and the degree of feature when estimating the usage situation.
An information processing device characterized by: The creation unit creates a correct persona in advance,
The estimation unit classifies the usage status of the online content of the correct persona in advance, compares the correct persona and the persona, and estimates the usage status of the persona,
2. The evaluation unit according to claim 1 , wherein, when the correct persona and the persona diverge, the evaluation unit scores improvements in the online content so that the persona approaches the correct persona. Information processing equipment. The creation unit creates a correct persona in advance,
The estimation unit classifies the usage status of the online content of the correct persona in advance, compares the correct persona and the persona, and estimates the usage status of the persona,
The information processing apparatus according to claim 1 , wherein, when the correct persona and the persona diverge, the estimation unit corrects the correct persona so that the correct persona approaches the persona. an acquisition unit for acquiring usage data based on access to online content;
a creation unit that creates a persona based on the actual usage data;
an estimation unit that estimates usage of the online content by the persona;
an evaluation unit that quantitatively evaluates the usability test of the online content when estimating the usage status;
with
The creation unit creates a correct persona in advance,
The estimation unit classifies the usage status of the online content of the correct persona in advance, compares the correct persona and the persona, and estimates the usage status of the persona,
When the correct persona deviates from the persona, the evaluation unit scores improvements in the online content so that the persona approaches the correct persona.
An information processing device characterized by: The creation unit creates a plurality of the correct personas in advance,
The estimation unit classifies the usage status of the online content for each of the plurality of correct personas in advance, and compares each of the plurality of correct personas with the persona to determine the usage status of the persona. 5. The information processing apparatus according to any one of claims 2 to 4 , characterized by estimating . an acquisition unit for acquiring usage data based on access to online content;
a creation unit that creates a persona based on the actual usage data;
an estimation unit that estimates usage of the online content by the persona;
an evaluation unit that quantitatively evaluates the usability test of the online content when estimating the usage status;
with
The creation unit creates a plurality of correct personas in advance,
The estimation unit classifies the usage status of the online content for each of the plurality of correct personas in advance, and compares each of the plurality of correct personas with the persona to determine the usage status of the persona. to estimate
An information processing device characterized by: The information processing apparatus according to claim 5 or 6 , wherein the evaluation unit performs an AB test of the plurality of correct personas, evaluates the correct personas, and converts them into scores. The estimating unit creates a learning device from the actual usage data used when creating the persona, and performs machine learning to estimate the usage status,
8. The evaluation unit evaluates whether the user operates the online content as expected from the accuracy of the learning device when the usage situation is classified . 1. The information processing device according to 1. an acquisition unit for acquiring usage data based on access to online content;
a creation unit that creates a persona based on the actual usage data;
an estimation unit that estimates usage of the online content by the persona;
an evaluation unit that quantitatively evaluates the usability test of the online content when estimating the usage status;
with
The estimating unit creates a learning device from the actual usage data used when creating the persona, and performs machine learning to estimate the usage status,
The evaluation unit evaluates whether the user operates the online content as expected from the accuracy of the learning device when the usage situation is classified.
An information processing device characterized by: An information processing method executed by an information processing device,
a obtaining step of obtaining usage data based on accessing online content;
a creation step of creating a persona based on the actual usage data;
an estimation step of estimating usage of the online content by the persona;
an evaluation step of quantitatively evaluating the usability test of the online content when estimating the usage status;
including
In the evaluation step, points to be improved in the online content are scored from the evaluation index and feature degree when the usage state is estimated.
An information processing method characterized by: a retrieval procedure for obtaining usage data based on access to online content;
a creation procedure for creating a persona based on the actual usage data;
an estimation procedure for estimating usage of the online content by the persona;
an evaluation procedure for quantitatively evaluating the usability test of the online content when estimating the usage status;
on the computer, and
In the evaluation procedure, the improvement points of the online content are scored from the evaluation index and feature degree when the usage situation is estimated.
An information processing program characterized by: An information processing method executed by an information processing device,
a obtaining step of obtaining usage data based on accessing online content;
a creation step of creating a persona based on the actual usage data;
an estimation step of estimating usage of the online content by the persona;
an evaluation step of quantitatively evaluating the usability test of the online content when estimating the usage status;
including
In the creation process, a correct persona is created in advance,
In the estimation step, the usage status of the online content of the correct persona is classified in advance, and the usage status of the persona is estimated by comparing the correct persona and the persona,
In the evaluation step, when the correct persona and the persona diverge, points to be improved in the online content are scored so that the persona approaches the correct persona.
An information processing method characterized by: a retrieval procedure for obtaining usage data based on access to online content;
a creation procedure for creating a persona based on the actual usage data;
an estimation procedure for estimating usage of the online content by the persona;
an evaluation procedure for quantitatively evaluating the usability test of the online content when estimating the usage status;
on the computer, and
In the above creation procedure, the correct persona is created in advance,
In the estimation procedure, the usage status of the online content of the correct persona is classified in advance, and the usage status of the persona is estimated by comparing the correct persona and the persona,
In the evaluation procedure, if the correct persona and the persona are diverging, points to be improved in the online content are scored so that the persona approaches the correct persona.
An information processing program characterized by: An information processing method executed by an information processing device,
a obtaining step of obtaining usage data based on accessing online content;
a creation step of creating a persona based on the actual usage data;
an estimation step of estimating usage of the online content by the persona;
an evaluation step of quantitatively evaluating the usability test of the online content when estimating the usage status;
including
In the creation step, a plurality of correct personas are created in advance,
In the estimating step, the usage status of the online content of each of the plurality of correct personas is classified in advance, and each of the plurality of correct personas is compared with the persona to determine the usage status of the persona. to estimate
An information processing method characterized by: a retrieval procedure for obtaining usage data based on access to online content;
a creation procedure for creating a persona based on the actual usage data;
an estimation procedure for estimating usage of the online content by the persona;
an evaluation procedure for quantitatively evaluating the usability test of the online content when estimating the usage status;
on the computer, and
In the creation procedure, multiple correct personas are created in advance,
In the estimating procedure, the usage status of the online content for each of the plurality of correct personas is classified in advance, and each of the plurality of correct personas is compared with the persona to determine the usage status of the persona. to estimate
An information processing program characterized by: An information processing method executed by an information processing device,
a obtaining step of obtaining usage data based on accessing online content;
a creation step of creating a persona based on the actual usage data;
an estimation step of estimating usage of the online content by the persona;
an evaluation step of quantitatively evaluating the usability test of the online content when estimating the usage status;
including
In the estimation step, a learning device is created from the actual usage data used when creating the persona, machine learning is performed to estimate the usage status,
In the evaluation step, whether or not the user operates the online content as expected is evaluated based on the accuracy of the learning device when the usage situation is classified.
An information processing method characterized by: a retrieval procedure for obtaining usage data based on access to online content;
a creation procedure for creating a persona based on the actual usage data;
an estimation procedure for estimating usage of the online content by the persona;
an evaluation procedure for quantitatively evaluating the usability test of the online content when estimating the usage status;
on the computer, and
In the estimation procedure, a learning device is created from the actual usage data used when creating the persona, machine learning is performed to estimate the usage status,
In the evaluation procedure, whether or not the user operates the online content as expected is evaluated based on the accuracy of the learning device when the usage situation is classified.
An information processing program characterized by:

Images